Abstract
Objectives
We describe an approach for analyzing failures in diagnostic processes in a small, enriched cohort of general medicine patients who expired during hospitalization and experienced medical error. Our objective was to delineate a systematic strategy for identifying frequent and significant failures in the diagnostic process to inform strategies for preventing adverse events due to diagnostic error.
Methods
Two clinicians independently reviewed detailed records of purposively sampled cases identified from established institutional case review forums and assessed the likelihood of diagnostic error using the Safer Dx instrument. Each reviewer used the modified Diagnostic Error Evaluation and Research (DEER) taxonomy, revised for acute care (41 possible failure points across six process dimensions), to characterize the frequency of failure points (FPs) and significant FPs in the diagnostic process.
Results
Of 166 cases with medical error, 16 were sampled: 13 (81.3%) had one or more diagnostic error(s), and a total of 113 FPs and 30 significant FPs were identified. A majority of significant FPs (63.3%) occurred in “Diagnostic Information and Patient Follow-up” and “Patient and Provider Encounter and Initial Assessment” process dimensions. Fourteen (87.5%) cases had a significant FP in at least one of these dimensions.
Conclusions
Failures in the diagnostic process occurred across multiple dimensions in our purposively sampled cohort. A systematic analytic approach incorporating the modified DEER taxonomy, revised for acute care, offered critical insights into key failures in the diagnostic process that could serve as potential targets for preventative interventions.
Funding source: Agency for Healthcare Research and Quality
Award Identifier / Grant number: R18HS026613
Acknowledgments
We would like to thank Drs. Marc Pimentel and Mallika Mendu and Dr. Nina Chalfin for providing cases from the Brigham and Women’s Hospital (BWH) Morbidity and Mortality review process, and Brigham Health’s Hospital Medicine Unit Quality Assurance committee, respectively.
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Research funding: This research is funded by AHRQ PSLL Award R18 - HS026613.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Competing interests: Dr. Dalal reports consulting fees from MayaMD, which makes AI software for patient engagement and decision support. Dr. Rozenblum reports having an equity in Hospitech Respiration Ltd, which makes Airway Management Solutions. Dr. Bates reports grants and personal fees from EarlySense, personal fees from CDI Negev, equity from ValeraHealth, equity from Clew, equity from MDClone, personal fees and equity from AESOP, and grants from IBM Watson Health, outside the submitted work. Authors otherwise report no conflicts of interest.
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Informed consent: Not applicable.
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Ethical approval: The local Institutional Review Board provided approval for the conduct of this study.
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Supplementary Material
The online version of this article offers supplementary material (https://doi.org/10.1515/dx-2021-0033).
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